Using DataSecOps To Maintain Data Security in the Cloud
Sep 21, 2022
Using DataSecOps To Maintain Data Security in the Cloud

Cloud adoption has become a particularly popular issue in the past couple of years due to the rise in remote work, even though firms have been progressively preparing cloud migrations for the past ten years. According to Gartner, global spending on risk management and IT solutions will increase by 12.4% in 2021. The data may be safe in the Cloud if you use a DataSecOps approach to cloud security. In this article, we will strive to explore the complete extent of DataSecOps and why it is so important for cloud security. 

Why does Cloud data security matter?

Cyberattacks are becoming more sophisticated, and data security is frequently compromised as a result of breaches. Small and medium-sized businesses are responsible for about half of all data breaches, despite the fact that large corporations are more frequently the target of severe cyberattacks.

Along with cost savings, the Cloud offers more productivity and flexibility. Cloud technology can also expose your company to a variety of complex cyber threats, though, if it's not managed effectively. Data protection is crucial because when businesses are laid back about security concerns, their infrastructure becomes vulnerable to issues like downtime, data loss, and financial losses as a result of regulatory proceedings.

What is DataSecOps, and why is it important?

DataSecOps is a change in how businesses approach security as a component of their data operations. It is the realization that security should not be an afterthought but rather an ongoing component of data operations activities. In actuality, DataSecOps should be seen as the mechanism that enables data democratization. DataSecOps provides a balance that allows teams within an organization to quickly produce value from data without sacrificing privacy, security, or accountability.

Main Principles of DataSecOps 

1. Shared Data Ownership

DataSecOps indicates that there is not just a shared thinking about security but also shared responsibility for security among different teams anytime data is involved. DataSecOps is also the recognition that many organizations now deal with a large amount of sensitive data and that this data in the wrong hands can cause significant harm. Security is both a major advantage and a huge liability for the organization due to privacy and data protection rules, as well as the possibility of data disclosure.

2. DataSecOps Prefers Continuous Processes over Impromptu Projects

Impromptu data projects involving security, privacy, and governance tend to grow old quickly because data today is continually changing, and new data objects and users emerge on a regular basis. A sensitive data discovery project, for example, undertaken once a year (or even once a quarter) may be problematic if the frequency of changes is significantly higher. Ideally, such projects should be replaced or supplemented by continuous processes whenever practicable.

3. Prioritization of Sensitive Data 

In data, you should nearly always prioritize sensitive data first because security issues with sensitive data can be, well, really sensitive.

Knowing where exactly your sensitive data is and what it contains is a requirement for prioritizing sensitive data (in order to prioritize within sensitive data). If you don't know where your sensitive data is, finding out might be an excellent place to start. Then, investigate what safeguards you can put in place to protect it, as well as who has access to it and whether or not that access may be canceled or limited (for example, by masking the sensitive data).

4. Quick Time-to-Value With Prompt Security

The final principle is to enable the company to be "data-driven," allowing for data democratization while maintaining security. We can achieve this ideal by ensuring that there are clear policies and processes in place surrounding data access. We can do this so that when there are some delays in getting access to the data, they are due to "the appropriate reasons" (i.e., an edge case that occurs infrequently and requires special approval) and not because of a manual long and complex process that requires all data customers to wait a long period of time to get access to data.

5. Utilize methods for protecting data analytics pipelines

One of the most noteworthy advantages of the Cloud is data analytics, which provides unmatched scale and uses insights for market differentiation. Organizations should naturally make sure that data is safeguarded at every stage of its lifespan via the pipeline, which calls for a variety of situational strategies.

Unstructured data that is being created must be categorized in order to decide how it should be secured. Identifying whether the data in question contains sensitive information, such as a Social Security number (SSN), home address, or credit card number, is the first step in categorizing it. The data can be hashed if sensitive information is found in it, but it doesn't need to be evaluated. The sensitive information is totally concealed using characters in a different format thanks to this method.

How Can DataSecOps Prevent Cloud Attacks? 

The DataSecOps strategy eliminates the need to remediate possible risks after they occur. This allows data scientists and IT teams to collaborate while creating the cloud architecture, with data protection as a key component, stitching security and privacy into the Cloud. 

According to DataSecOps, good and agile data governance is an essential component of a healthy and safe data operation. This means that data should have distinct owners and be accessible in a secure but straightforward manner.

Additionally, DataSecOps recognizes that automation and testing are important factors in distinguishing a successful and secure data operation from a failing one. Manual labor may result in a bottleneck or higher dangers because small teams are handling big volumes of data activities.

Bottom Line

DataSecOps is comprehensive because it addresses all data-related processes, not just those involving specific teams. It is not just about the data engineering team, for example, and does not imply that once another team pulls data, they are "out of scope" for DataSecOps operations. Cloud data security differs significantly from on-premise security in many ways. Having the correct strategies in place can help prevent or substantially reduce the effect of a breach while still preserving the business value of data. As a result, DataSecOps should be a cornerstone of any organization's cloud security strategy.